Electronic commerce via the World Wide Web (“web”) is becoming an increasingly popular way for people to buy products. The people who use the web to buy products often turn to the web for help in deciding what products to buy and from which web retailer. Many sources of product reviews are available via the web to help a person. These product reviews may be provided by professional product review web sites (e.g., CNet), commercial web sites (e.g., Amazon.com), discussion boards, and personal web sites and web logs (“blogs”). A person can use any of these sources of product reviews to help in their buying decision.
Product reviews may be generated by experts or by customers of retail web sites. A professional product review web site may enlist the services of various experts to review products or services that may include movies, music, books, automobiles, electronic products, software, and so on. These experts review the products and provide their opinion on the product via a product review. Many of these professional product review web sites generate significant revenue from advertisements that are presented along with their reviews. To increase traffic to their web sites, these professional product review web sites typically try to ensure the quality of their reviews. In contrast, some retail web sites allow any customer to submit a product review, but may exert no control over the quality and accuracy of the product reviews.
In addition to helping potential buyers make buying decisions, these product reviews may provide valuable feedback to manufacturers who seek to improve the quality of their products. The product reviews provide a wealth of information relating to what experts and customers like and dislike about a manufacturer's products. Because of the large volume of product reviews being created, it can be very difficult and time-consuming for a manufacturer to identify all the product reviews for the manufacturer's products and then to categorize the product reviews as expressing a positive or negative opinion about the product.
Although some attempts have been made to classify product reviews as being positive or negative, these attempts typically try to classify product reviews by applying text classification techniques. These attempts generate training data by classifying product reviews as being positive or negative. The attempts then extract features and train a classifier to classify product reviews based on the features of the reviews. Text classification techniques, however, may not be particularly effective at classifying product reviews. Text classification techniques rely, in large part, on term frequency to identify the topics of a document. Since the opinion of a product review may be expressed clearly only in a single sentence of a long product review, the use of term frequency may not help identify the opinion. Also, some product reviews may have their opinions expressed indirectly or may even attempt to mask their opinion. In such cases, text classification techniques will likely not be able to correctly classify the opinions expressed by the product reviews.